Foundational Data Engineering At Two Sigma
Data Engineering Podcast
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Evolving Data Dynamics
This chapter discusses the transformation of data from a basic utility to a central product driving business decisions. It highlights the importance of collaboration and continuous learning in navigating the rapidly changing data technology landscape.
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Summary
In this episode of the Data Engineering Podcast Effie Baram, a leader in foundational data engineering at Two Sigma, talks about the complexities and innovations in data engineering within the finance sector. She discusses the critical role of data at Two Sigma, balancing data quality with delivery speed, and the socio-technical challenges of building a foundational data platform that supports research and operational needs while maintaining regulatory compliance and data quality. Effie also shares insights into treating data as code, leveraging modern data warehouses, and the evolving role of data engineers in a rapidly changing technological landscape.
Announcements
Parting Question
In this episode of the Data Engineering Podcast Effie Baram, a leader in foundational data engineering at Two Sigma, talks about the complexities and innovations in data engineering within the finance sector. She discusses the critical role of data at Two Sigma, balancing data quality with delivery speed, and the socio-technical challenges of building a foundational data platform that supports research and operational needs while maintaining regulatory compliance and data quality. Effie also shares insights into treating data as code, leveraging modern data warehouses, and the evolving role of data engineers in a rapidly changing technological landscape.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I'm interviewing Effie Baram about data engineering in the finance sector
- Introduction
- How did you get involved in the area of data management?
- Can you start by outlining the role of data in the context of Two Sigma?
- What are some of the key characteristics of the types of data sources that you work with?
- Your role is leading "foundational data engineering" at Two Sigma. Can you unpack that title and how it shapes the ways that you think about what you build?
- How does the concept of "foundational data" influence the ways that the business thinks about the organizational patterns around data?
- Given the regulatory environment around finance, how does that impact the ways that you think about the "what" and "how" of the data that you deliver to data consumers?
- Being the foundational team for data use at Two Sigma, how have you approached the design and architecture of your technical systems?
- How do you think about the boundaries between your responsibilities and the rest of the organization?
- What are the design patterns that you have found most helpful in empowering data consumers to build on top of your work?
- What are some of the elements of sociotechnical friction that have been most challenging to address?
- What are the most interesting, innovative, or unexpected ways that you have seen the ideas around "foundational data" applied in your organization?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working with financial data?
- When is a foundational data team the wrong approach?
- What do you have planned for the future of your platform design?
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
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- 2Sigma
- Reliability Engineering
- SLA == Service-Level Agreement
- Airflow
- Parquet File Format
- BigQuery
- Snowflake
- dbt
- Gemini Assist
- MCP == Model Context Protocol
- dtrace
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